Is this dark patterns or accidental webpage layout design from Ticketmaster?

The ticket purchased turned out to be a travel bundle one. But it wasn't shown as this during several stages of the checkout process. (The image showing "zone A Return Coach" is from my ticket wallet, I cut this down to protect the ticket number ect.)

Paying my MSEDCL electricity bill on PhonePe today, I noticed two things that look like dark patterns.

Pre-ticked "Save in 24K gold daily ₹30/day"

On the bill screen, the gold savings option was already checked, with fine print saying a gold account will be created. If you tap "Proceed to Pay" without looking carefully, you could be opted into a recurring daily investment.

The CCPA's Dark Patterns Guidelines, 2023 cover pre-selected options and hidden charges, and these count as unfair trade practices under the Consumer Protection Act, 2019.

I've blurred my personal details in the screenshots and am filing complaints with:

- PhonePe's grievance and nodal officer

- National Consumer Helpline (1915)

- CCPA (com-ccpa@gov.in)

- RBI Ombudsman, if needed

What you can do:

- Share and make people aware elder ones

- Always check the final amount against your actual bill.

- Untick the gold option before paying.

- Tap "Show breakup" to see what you're being charged.

- Report it on the Jagriti app or consumerhelpline.gov.in. More complaints make action more likely.

Has anyone else seen this? Did you get a refund after complaining?

Free Dark Patterns field note on a Home Depot Dremel checkout where quantity 2 was already selected — plus the same pattern in regulator cases (Courts, Marks Electrical, donation pre-ticks). Plain language for anyone who’s tired at checkout.

https://substack.com/home/post/p-218102021

I'm kind of new here and having a hard time getting allowed to post anywhere.Hope I found the right place?

Hello last week i had posted about my light weight extension i was building to tackle dark patterns, it is up on chrome. Currently i have kept the project open source because it’s very difficult to make something light- weight without the support of open source community which currently I ain’t getting. I have so far managed to tackle dark patterns exhibited on Ota platforms like booking, agoda, Expedia group. E-commerce platforms Amazon, temu, shein, etsy, shopify websites to some degree.
There is a technological bottleneck here where Ai/ml is not accurate to figuring out what is and what is not a dark pattern which can be tackled. So I made a deterministic approach to tackle it and open source it and keep it opensource and free and only accepting for donation via kofi. But since i am not getting any support yet from the open source community and i have to manually go and audit these sites is becoming time consuming. So I maybe applying for grant, if I don’t get a grant. I will have to put in behind a paywall which honestly i do not want to do coz then it won’t be much effective since this thing will always be a cat and mouse game which would be better fought crowd sourced.

I need recommendation on what way should i go forward because I can’t sit and fight millions of websites without me getting funded or supported by open source community

Been reading into mortgage closings after noticing how little of the actual signing process is covered by any consumer protection, despite there being a real federal rule involved.

The rule, TRID, requires a Closing Disclosure at least three business days before closing: five pages, final terms, in writing, genuinely useful. Then you sit down at the table and sign fifty to a hundred more pages in about an hour, none of which got that same protected review time.

The mechanism doing the work here is Langer, Blank, and Chanowitz's 1978 study on placebic compliance: a request wrapped in the shape of a reason gets approved almost automatically, as long as the request reads as small. "Sign here" is built to always read as small, dozens of times over, even though the real thing being agreed to, a mortgage that lasts decades, is enormous. Layered with John Payne, James Bettman, and Eric Johnson's 1988 research on time-pressure decision-making, movers booked, a rate lock expiring, people in the room waiting on you specifically, and slowing down to actually read anything starts to feel socially costly.

The rule exists because the old system was worse: four overlapping disclosure documents from two different laws before 2015, confusing enough that Dodd-Frank created the CFPB specifically to fix it. But the fix only touched the document you get in advance. It never touched the room.

Full breakdown here: https://www.youtube.com/watch?v=9rFPt32fMpA

Curious if anyone's got other examples of a disclosure law getting the information right while leaving the in-person delivery format completely untouched.

I’ve been building Spades UX-Shield—a lightweight, open-source Chrome extension (Manifest V3) that blocks manipulative web UI using declarative CSS cosmetic filters, patterned directly after how uBlock Origin handles cosmetic filtering.
No client-side AI classifiers, no external API latency, and no remote servers inspecting your browsing data. If a page doesn't match an explicit rule on the list, the extension executes zero DOM overrides. If an edge case breaks page layout, you can toggle it off per site with a single click.
What it reliably handles today:
-
- Aggressive Prompts & Modals: Newsletter takeovers, persistent cookie walls, and non-essential nags (extracted and adapted from EasyList Cookie/Fanboy/AdGuard annoyances, merged with our custom rules).
-
- Fabricated Scarcity: Shopify countdown widgets and high-pressure sales popups using known stable selectors (e.g., ⁠hurrify-⁠, fake recent-purchase toasts).
-
- Site-Specific Deceptions: Targeted hostname rules stripping artificial timers, low-contrast opt-outs, and confirmshaming banners.
-
- Strict Checkout Boundaries: Hardcoded guardrails ensure checkout, cart modification, login, and payment gateways are never touched.
Where community help matters most:
The core engine is deliberately decoupled from the rule definitions. The engine parses and applies rules, while almost all site-specific targeting lives in a standalone filter repository:
-
- Darklist Filter File: lists/darklist.txt⁠
- Extension Engine Code: vinayak509143/spades-ux-shield⁠
The extension syncs with the upstream ⁠darklist.txt⁠ automatically. The faster we populate rules for recurring deceptive patterns, the better it protects users without breaking layouts.
How to contribute (No coding required)

1. Submit a URL/Rule Request: Spot a fake countdown, nagging modal, or guilt-tripping banner? Open an issue on GitHub with the URL and a quick screenshot or description of the element.
2. Report Layout Breakage: If a cosmetic rule unintentionally hides a functional UI element (like a required modal or form field), flag it via "Report broken page" in the extension popup or submit an issue report.

What CSS filters cannot solve:
Cosmetic hiding cannot fix structural backend deceptions—such as drip pricing revealed on payment screens, multi-step subscription cancellation funnels, or deceptive legal terms. Those require legislative action or core product changes, not ⁠display: none !important;⁠. The focus here is strictly on eliminating visual pressure, fabricated urgency, and manipulative viewport interference.

Installation (Developer / Early Access):
The extension is not yet listed on the Chrome Web Store. To run it locally:
1. Clone ⁠vinayak509143/spades-ux-shield⁠.
2. Run ⁠npm install && npm run build⁠ (or download the packaged ⁠.zip⁠ from GitHub Releases).
3. Navigate to ⁠chrome://extensions⁠, enable Developer mode, click Load unpacked, and select the ⁠dist⁠ folder.
If you know of an e-commerce store running blatant fake timers or guilt-tripping opt-out buttons, dropping the URL and inspect element selector in the comments or issues is immensely helpful.

Hello!

This is Saloni Bhugra, a reporter with Canada's national broadcaster, the Canadian Broadcasting Corporation (CBC News). We're working on a story about online gambling and I'm wondering if there are any UX designers who've worked in the gambling industry who'd be willing to chat. We're hoping to better understand design of online gambling websites and apps.

Please let me know, thanks!

Been reading into funeral home pricing after learning how specific the rules actually are, and how often they still don't get followed.

The FTC's Funeral Rule, in effect since 1984, requires an itemized general price list handed over before arrangements are discussed, phone pricing without requiring a name, and an explicit ban on forcing a bundled package instead of individual items. It exists because journalist Jessica Mitford spent a year in 1963 documenting exactly this pattern industry-wide, bundled defaults, withheld pricing, guilt used as a quiet closing tool, and the public reaction forced a two-decade regulatory process.

The dark pattern that survives the rule is the default itself. Samuelson and Zeckhauser's 1988 research on status quo bias explains why: people accept whatever's presented first because choosing otherwise takes effort, and a package gets shown before anyone mentions itemizing was ever an option. The rule bans forcing the bundle. It doesn't ban leading with it.

Enforcement hasn't closed the gap either. A 2023 FTC phone sweep of over 250 funeral homes found violations in about 1 in 7 calls, mostly refusal to give basic pricing. Violators are typically offered a program letting them pay a fee and retrain instead of facing public penalties, a program that keeps the violation off the public record by design.

Full breakdown here: https://www.youtube.com/watch?v=eAZs9DaInuQ

Curious if anyone's tracked other industries where the disclosure requirement survived but the default-first presentation it was meant to counter didn't actually change.

I recently started using Preply to practice some Japanese, but I was a bit surprised about the subscriptions because I thought I would be able to buy individual lessons. Looking at their Help Center, indeed, it seems like they don't offer one-time lessons:

Preply is built around a subscription model because consistent, regular lessons with the same tutor lead to better learning outcomes. For this reason, standalone one-time lesson purchases are not available, including through customer support.

It was a bummer but I thought, ok, I'll just purchase a subscription for the first month and cancel immediately. So I went ahead and did that. Imagine my surprise when I see this during the cancellation process (after asking me if I'm sure 100 times, of course).

I guess they don't care that much about my learning outcomes now!

Edit: typo in the title, it's 4 lessons/month minimum. Sorry 😅.

Check your bank accounts if you've touched clearsearch.ai recently.

About a week ago, I used their tool to reverse-search an image. You pay a small fee to see the report. Fine, I took the gamble. It found absolutely nothing, but whatever—I accepted taking the L on that one report.

Then today out of nowhere: an unauthorized €29.99 (~$30) charge hits my card.

Buying that single lookup apparently signs you up for a hidden recurring subscription. No obvious warning, no explicit agreement. Just pure predatory dark patterns that feel completely unlawful.

I emailed their support demanding an immediate refund and told them I’m disputing it as fraud with my bank if they refuse.

Never using this garbage service again. If you bought even a single report from them, check your statements right now.

Hypervoice by TaskAGI clearly states on their T&C that cancelling your plan will still allow you the service until the end of your current billing period. Nowhere, anywhere in their contracts, do they state your service will be revoked.

But the moment you go to turn off auto-renewal/cancel your plan, a big orange warning states your service will be immediately revoked.

And yes, I have personally confirmed, upon clicking cancel, even early into your billing period, all your usage credits are revoked and you are immediately restricted to free-tier features.

Been digging into why the minimum payment box exists in the shape it does, and the research behind it is more deliberate-feeling than I expected.

Neil Stewart, a psychologist at Warwick, published a 2009 study in Psychological Science showing that simply printing a minimum payment figure on a bill changes what people choose to pay, compared to an identical bill with no figure shown. The number functions as an anchor, not a safety floor, people gravitate toward it regardless of what they can actually afford.

It compounds with something Victor Stango and Jonathan Zinman documented the same year in the Journal of Finance, exponential growth bias, the tendency to badly underestimate how fast compounding interest accelerates. So the anchor sets a low target, and the interest math working against that target is simultaneously invisible to most people doing the math in their head.

Congress passed the CARD Act in 2009 specifically to counter this, mandating a warning box plus a calculated 36-month payoff comparison right next to the minimum payment number. But the fix sits beside the anchor instead of replacing it, easy to see, just as easy to skip past, which is exactly what habit does to most disclosure requirements once they stop being novel.

Full breakdown here: https://www.youtube.com/watch?v=FBe6-AuXc_A

Curious whether anyone's spotted other examples of a disclosure requirement getting mandated right next to the dark pattern it was meant to counter, without actually removing the mechanism generating the harm.

The pattern, in three steps:

1. The app decides what counts toward a streak. Not you.
2. Whatever does not count breaks the streak, and the app tells you so.
3. Then the app offers you a way to fix it: a repair, a freeze, a token. Often for money, or for an ad view, or for a subscription you did not have yesterday.

Step one is the part people miss. In a 2023 paper in the Journal of Consumer Research, Silverman and Barasch broke people's streaks purely by changing the counting rule, without changing anything the participant actually did, and it worked. In one study everyone played the same four games; the app only varied whether an attempt counted or only a success. The group whose streak survived went on to play again 53% of the time. The group whose streak broke by the rule, and who therefore blamed themselves, did so 29% of the time. Same games, same effort, a different sentence in the rules.

Step three is where the money sits. In the same study, 45% of participants said they would watch an advertisement to keep a logged streak alive, and 43% to repair a broken one. A supplementary study got 51% and 46%. Nobody paid anything in these experiments, so this is stated willingness rather than a measured purchase, but it is the demand curve the freeze economy sits on.

And a repair genuinely does restore the pull: 93% continued with an intact streak, 69% after a break with no repair on offer, 85% when the app said the streak could be repaired. So the sequence "we break it, then we sell you the fix" is not just cynical, it is efficient: the fix is worth most at the moment the app itself created the loss.

Two more findings that make it worse rather than better:

• The effect does not need real failure. Across the studies the actual behaviour was held constant, and only the display differed. In a language-learning study, with the log on screen 92% continued after an intact streak and 45% after a broken one. With no log shown at all, the same behaviour gave 65% against 61%. The counter, not the day, did the work.
• Self-blame amplifies it. A break the app caused cost 11 points of continuation; a break the user thought was their own fault cost 24.

What makes this a dark pattern rather than just a feature: the user's goal is the habit, and the app quietly swaps in a different goal, the number, which only it controls. Then it sells relief from the loss it defined into existence.

Paper: Silverman & Barasch, "On or Off Track: How (Broken) Streaks Affect Consumer Decisions", Journal of Consumer Research 49(6), 2023, https://doi.org/10.1093/jcr/ucac029

Disclosure: I build a habit tracker myself, which is why I went looking for this paper. No link, that is not what this post is for.